Abstract

The material removal in grinding involves rubbing, ploughing and cutting. For grindingprocess monitoring, it is important to identify the effects of these different phenomena experiencedduring grinding. A fundamental investigation has been made with single grit cutting tests. AcousticEmission (AE) signals would give the information relating to the groove profile in terms of materialremoval and deformation. A combination of filters, Short-Time Fourier Transform (STFT), WaveletsTransform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and timeconstant measurements provided the principle components for classifying the different grindingphenomena. Identification of different grinding phenomena was achieved from the principlecomponents being trained and tested against a Neural Network (NN) representation.